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We investigate the case of a single decision maker (DM) who obtains probabilistic forecasts regarding the occurrence of a unique target event from J distinct, symmetric, and equally diagnostic expert advisors (judges). The paper begins with a mathematical model of DM's aggregation process of expert opinions, in which confidence in the final aggregate is shown to be inversely related to its perceived variance. As such, confidence is expected to vary as a function of factors such as the number of experts, the total number of cues, the fraction of cues available to each expert, the level of inter-expert overlap in information, and the range of experts' opinions. In the second part of the paper, we present results from two experiments that support the main (ordinal) predictions of the model.
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